For most people, generative AI begins with a simple chatbot like ChatGPT. You ask a question and receive an appropriate answer—it’s free, convenient, clear, and accessible. However, years after the introduction of ChatGPT, several factors must now be considered: hallucinations, privacy, security, rising token costs, compliance, legally obtained content, and reputational damage.

As AI technology grows, it becomes increasingly complex, expensive, and risky. This is why having a trusted partner is essential. Weconomics has over thirty-five years of experience with disruptive technologies such as the internet, blockchain, and AI. What ties all these technologies together? It’s the foundational principle of trust! And we know how to organize trust.

“A house is only as strong as its foundation. That’s what companies are quickly coming to understand about vertical AI agents as well.”

We understand the journey from simplicity to complexity. That is how ‘the factory’ also started: straightforward and clear. The ‘car in the making’ was pulled along by a rope. Workers no longer walked to the vehicle for generic artisanal tasks. No, the vehicle came to the workers, who performed a few simple tasks. After that, the product moved on to the next station. That was a different mindset and a different operating model. Started simple and straightforward, with a few system components.

Today, the situation is drastically different. Factories utilize robots to assemble cars almost entirely automatically. This advancement requires a coordinated ecosystem of suppliers and an orchestrator to coordinate and integrate: the assembly line. And that is exactly what Weconomics Technologies also delivers, but then for a digital environment.

Weconomics introduces the solution for IT silos: the digital assembly line. As a system architect & integrator, Weconomics ensures collaboration, coordination, and integration of individual components. A key aspect of our offering is a platform for AI quality assurance.

Quality and reliability are increasingly important, especially as more staff members and business service providers seek to use AI for professional research, analyses, advice and support. In regulated sectors, such as accounting, reliability, explainability, traceability, and compliance with laws and regulations are essential.

Existing generic AI solutions often fail to provide integrated support in these areas. Many organizations lack an independent, configurable way to guarantee the quality of AI outcomes. Vertical AI agents equipped with a quality assurance system can address this challenge.

Technological advancements are rapidly evolving, making it difficult for organizations to make informed strategic decisions. Should you rely on your existing software vendor? Does that vendor leverage existing insights, infrastructures, and legacy systems? Furthermore, do they possess the right expertise and DNA? Can AI be used reliably and efficiently with add-ons on your current IT systems? The answer is likely no.

While almost 95 percent of organizations are using or experimenting with AI, many struggle to leverage it for growth or cost reduction. Often, AI is primarily used to automate existing tasks. While automation can reduce costs, it does not facilitate significant change.

In fact, automation is becoming less a source of efficiency and productivity improvements and more a source of rising costs, digital waste, insecurity, inequality, risks and frustration. If we continue to organize AI as we have with automation in recent decades, we only risk exacerbating these issues. Without a shift in mindset, operational models, and infrastructures (as in the Ford Motor Company around 1900), AI will not solve our problems; it may intensify them.

Additionally, social media and AI generate an overwhelming amount of information, leading to information inflation. Staff members and business service providers often struggle to filter relevant information for their clients and translate it into meaningful decisions.

The Weconomics Pinaxx platform utilizes metadata to tag and curate abundant information, linking reliable, relevant external content to specific contexts, problems, and challenges. Moreover, the platform incorporates a component that uses AI to structure often inconsistent and unstructured internal data from administration documents, PowerPoints, flowcharts, memos, and policy notes.

Weconomics Technologies is focused on developing technology for reliable vertical AI agents. In this role, we act as both system architect and system integrator within an open ecosystem of specialized technology and content suppliers, and business partners.

Creating and implementing vertical AI agents requires integrating dozens of specialized components, including language models, knowledge sources, retrieval systems, evaluation models, governance components, and user interfaces. While many of these components are available individually, essential software elements for quality assurance, evaluation, provenance, monitoring, and integration remain lacking. Addressing these gaps is at the core of Weconomics Technologies’ mission and core activities.

Weconomics Technologies core activities can be categorized into:

Research & Strategy

• Investigating technological developments, customer needs, and validating new concepts.
• Exploring existing integrable components for the digital assembly line, including those for quality assurance, evaluation, governance, and monitoring.
• Identifying missing components necessary for reliable vertical AI agents.
• Researching integration architecture that allows various components to function as a cohesive and reliable system.
• Developing strategies with customer / Community of Practice.

System Development Lifecycle
(Ideation, Designing, Engineering, Developing, Integrating and Testing)

• Overall system architecture for vertical AI agents.
• Components sourced from specialized suppliers and developed ourselves.
• Mechanisms for data foundation (provenance, lineage, governance, management).
• Mechanisms for continuous quality monitoring, evaluation, and improvement.
• Software for ensuring reliability, explainability, and legality throughout the entire supply chain.

Data Supply Chain Management & Ecosystems

• Building and managing a network of technology and content providers, knowledge and business partners.
• Establishing architectural standards and quality criteria.
• Creating agreements concerning knowledge sharing, licensing, and value distribution.
• Facilitating scalable collaboration within a real open AI ecosystem.

Quality Assurance

• Setting up datasets, evaluation sets, and test scenarios, along with collecting user feedback.
• Committing to the continuous improvement of vertical AI agents based on real-world data.
• Developing and coordinating a quality management platform.

Are you curious to learn more? If so, schedule a quick introductory meeting by clicking this link.